By the end of this course, learners are provided a high-level overview of data analysis and visualization tools, and are prepared to discuss best practices and develop an ensuing action plan that addresses key discoveries. It begins with common hurdles that obstruct adoption of a data-driven culture before introducing data analysis tools (R software, Minitab, MATLAB, and Python). Deeper examination is spent on statistical process control (SPC), which is a method for studying variation over time. The course also addresses do’s and don’ts of presenting data visually, visualization software (Tableau, Excel, Power BI), and creating a data story.


Data Analysis and Visualization
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Data Analysis and Visualization
This course is part of Data-Driven Decision Making (DDDM) Specialization


Instructors: Peter Baumgartner
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What you'll learn
Identify stakeholders and key components imperative to an analytics project plan
Name strengths and weaknesses of different analysis and visualization tools
Visually identify, monitor, and remove process variation
Explain how to create a compelling data story
Skills you'll gain
- Data Analysis Software
- Data Visualization Software
- Business Planning
- Statistical Analysis
- Tableau Software
- Process Capability
- Statistical Visualization
- Data Cleansing
- Data Presentation
- Statistical Process Controls
- Data Analysis
- Data-Driven Decision-Making
- Data Storytelling
- Business Analytics
- Process Analysis
- Data Strategy
- Data Quality
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University of Colorado Boulder
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Showing 3 of 172
Reviewed on Dec 15, 2020
Examples and better instructions on the assignment would improve this course.
Reviewed on Nov 25, 2024
A great course indeed. Increased my knowledge of how to analyze data and the tools to use for the analyses.
Reviewed on Sep 11, 2022
VCe cours a ete tres important pour moi, car j'ai pu aprendre d'excellentes tectniques liees a l'analyse et visualisation de donnees, aisnique a la conception de plan d'action de projet.
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